However, more generally and flexibly, generalised posterior distributions have been proposed for loss functions given by, for example, composite likelihood [1]}, [2]}, [3]}, restricted likelihood [4]}, partial likelihood [5]} and quasi-likelihood [6]}. In particular, we consider a general Bayesian \(L^2\)  calibration approach using (REF ) where the loss function \(\ell (\theta ; \mathbf {y})\)  is a functional of \(\ell _{TW}(\theta ; \mathbf {y})\) .
